• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.24 no.10
• /
• pp.163-173
• /
• 2010

In multi-mobile robot environment, path planning and collision avoidance are important issue to perform a given task collaboratively and cooperatively. The proposed approach is based on a potential field method and fuzzy logic system. For a global path planner, potential field method is employed to select proper path of a corresponding robot and fuzzy logic system is utilized to avoid collisions with static or dynamic obstacles around the robot. This process is continued until the corresponding target of each robot is reached. To test this method, several simulation-based experimental results are given. The results show that the path planning and collision avoidance strategies are effective and useful for multi-mobile robot systems.

• 제어로봇시스템학회:학술대회논문집
• /
• 2004.08a
• /
• pp.389-394
• /
• 2004

A Fuzzy Logic Sliding Mode Control or FLSMC for the uninterruptible power system (UPS) is presented, which is tracking a sinusoidal ac voltage with specified frequency and amplitude. The FLSMC algorithm combines feedforward strategy with the Variable Structure Control (VSC) or Sliding Mode Control (SMC) and fuzzy logic control. The control function is derived to guarantee the existence of a sliding mode. FLSMC has an advantage that the stability of FLSMC can be proved easily in terms of VSC. Furthermore, the rules of the proposed FLSMC are independent of the number of system state variables because the input of the suggested controller is fuzzy quantity sliding surface value. Hence the rules of the proposed FLSMC can be reduced. The simulation results illustrate that the purposed approach gives a significant improvement on the tracking performances. It has the small overshoot in the transient and the smaller chattering in the steady state than the conventional VSC. Moreover, its can achieve the requirements of robustness and can supply a high-quality voltage power source in the presence of plant parameter variations, external load disturbances and nonlinear dynamic interactions.

• 제어로봇시스템학회:학술대회논문집
• /
• 1997.10a
• /
• pp.131-134
• /
• 1997

This paper presents a new approach to the design of intelligent control system for track vehicle system using fuzzy logic based on neural network. The proposed control scheme uses a Gaussian function as a unit function in the neural network-fuzzy, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is illustrated by simulation for trajectory tracking of track vehicle speed.

• Journal of Mechanical Science and Technology
• /
• v.15 no.12
• /
• pp.1664-1675
• /
• 2001

In this paper, the development of a new group controller for high-speed elevators is described utilizing the approach of adaptive dual fuzzy logic. Some goals of the control are to minimize the waiting time, mean-waiting time and long-waiting time in a building. When a new hall call is generated, all adaptive dual fuzzy controller evaluates the traffic patterns and changes the membership function of a fuzzy rule base appropriately. A control algorithm is essential to control the cooperation of multiple elevators in a group and the most critical control function in the group controller is an effective and proper hall call assignment of the elevators. The group elevator system utilizing adaptive dual fuzzy control clearly performs more effectively than previous group controllers.

• Journal of Power Electronics
• /
• v.12 no.5
• /
• pp.821-829
• /
• 2012

This research article presents a novel approach based on an instantaneous active and reactive power component (p-q) theory for generating reference currents for shunt active filter (SHAF). Three-phase reference current waveforms generated by proposed scheme are tracked by the three-phase voltage source converter in a hysteresis band control scheme. The performance of the SHAF using the p-q control strategy has been evaluated under various source conditions. The performance of the proposed control strategy has been evaluated in terms of harmonic mitigation and DC link voltage regulation. In order to maintain DC link voltage constant and to generate the compensating reference currents, we have developed Fuzzy logic controller with different (Trapezoidal, Triangular and Gaussian) fuzzy M.F.s. The proposed SHAF with different fuzzy M.F.s is able to eliminate the uncertainty in the system and SHAF gains outstanding compensation abilities. The detailed simulation results using MATLAB/SIMULINK software are presented to support the feasibility of proposed control strategy. To validate the proposed approach, the system is also implemented on a real time digital simulator and adequate results are reported for its verifications.

• Journal of Power Electronics
• /
• v.2 no.4
• /
• pp.305-311
• /
• 2002

In this paper, two approaches for designing tracking controllers for a variable reluctance motor (VRM), namely the Lyapunov-based fuzzy approach and the classical approach, are compared. The nonlinear model of a VRM is first addressed. The two control schemes are introduced afterwards, and then applied to obtain tracking controllers. Simulation results of a sample case, to which the methods are applied, are also presented. Comparison of the methods based on the results obtained concludes the paper.

• Journal of Electrical Engineering and Technology
• /
• v.7 no.4
• /
• pp.546-557
• /
• 2012

In Conventional Combined Vector and Direct Controls (VC-DTC) of induction motor, stator current is very rich in harmonic components. It leads to high torque ripple of induction motor in high and low speed region. To solve this problem, a control method based on the concept of fuzzy logic approach is used. The control scheme proposed uses stator current error as variable. Through the fuzzy logic controller rules, the choice of voltage space vector is optimized and then torque and speed are controlled successfully with a less ripple level in torque response, which improve the system's performance. Simulation results trough MATLAB/SIMULINK${(R)}$ software gave results that justify the claims.

• Nuclear Engineering and Technology
• /
• v.44 no.4
• /
• pp.363-368
• /
• 2012

A feedwater controller targeted for an ABWR has been implemented using a modern field programmable gate array (FPGA), and verified using the full scope simulator at Taipower's Lungmen nuclear power station. The adopted control algorithm is a rule-based fuzzy logic. Point to point validation of the FPGA circuit board has been executed using a digital pattern generator. The simulation model of the simulator was employed for verification and validation of the controller design under various plant initial conditions. The transient response and the steady state tracking ability were evaluated and showed satisfactory results. The present work has demonstrated that the FPGA based approach incorporated with a rule-based fuzzy logic control algorithm is a flexible yet feasible approach for feedwater controller design in nuclear power plant applications.

• Proceedings of the IEEK Conference
• /
• summer
• /
• pp.119-121
• /
• 2004

Video event detection has become an essential application in multimedia computing. For sports video, salient events are usually detected by analyzing video sequence by specific decision rules. However in many kinds of sports video (e.g. soccer), the game contains continuous actions, in which the boundaries of shots, scenes are uncertain. So the conventional analyzing methods using crisp decisions are not efficient. Fuzzy logic is a natural approach that can tackle this problem. In this paper, we present a new approach using fuzzy technique for event detection in soccer video. The experiment shows encouraging results for this method

• Journal of the Korean Institute of Intelligent Systems
• /
• v.7 no.2
• /
• pp.3-15
• /
• 1997

In order to apply fuzzy logic, two major tasks need to be performed: the derivation of fuzzy rules and the determination of membership functions. These tasks are often difficult and time-consuming. This paper presents an algorithmic method for generating membership functions and fuzzy rules applicable to decision-making problems; the method includes an entropy minimization for clustering analog samples. Membership functions are derived by partitioning the variables into desired number of fuzzy terms, and fuzzy rules are obtained using minimum entropy clustering. In the mle derivation process, rule weights are also calculated. Inference and defuzzification for classification problems are also discussed.